基于自适应提升的概率矩阵分解算法

Probabilistic matrix factorization algorithm based on Ada Boost

针对推荐系统中概率矩阵分解模型(PMF)泛化能力(对新用户和物品的推荐性能)较差、预测准确性不高的问题,提出一种新的基于自适应提升的概率矩阵分解算法(AdaBoost PMF)。该算法首先为每个样本分配样本权重;然后根据PMF中的每一轮随机梯度下降法学习用户和物品特征向量,并计算总体预测误差均值和标准差。从全局的角度利用AdaBoost思想自适应调整样本权重,使算法更注重学习预测误差较大的样本;最后对预测误差分配样本权重,让用户和物品特征向量找到更合适的优化方向。相比传统的PMF算法,AdaBoost PMF算法能够将预测精度平均提高约2.5%。实验结果表明,该算法通过加权预测误差较大的样本,能够较好地拟合用户特征向量和物品特征向量,提高预测精度,可以有效地应用于研究个性化推荐。

Concerning the poor generalization ability（ the recommended performance for new users and items） and low predictive accuracy of Probabilistic Matrix Factorization（ PMF） in recommendation system, a new algorithm of Probabilistic Matrix Factorization algorithm based on Ada Boost（ AdaBoostPMF） was proposed. Firstly, the initial weight for each sample was assigned. Secondly, the feature vectors of users and items were learned by each round of PMF stochastic gradient descent method and the global mean and standard deviation of the prediction error were calculated. The sample weights were adaptively adjusted by using Ada Boost from the a global perspective, which made the proposed algorithm pay more attention to training those samples with the larger prediction error than others. Finally, the sample weights were assigned to predictive error, which found the more appropriate optimum direction for feature vectors of users and items. Compared with traditional PMF algorithm,the proposed AdaBoostPMF algorithm could significantly improve the prediction precision by about 2. 5% on average. The experimental results show that, the proposed algorithm can better fit the user feature vector and the item feature vector and improve the prediction accuracy by weighting the samples with larger prediction error. The proposed algorithm can be effectively applied to the personalized recommendation.